What is data collection in the context of data science?

• Analyzing and interpreting data

• Storing and organizing data

• Manipulating and transforming data

• Gathering and acquiring data from various sources.

Why is data cleaning important in data science?

• Data cleaning is important in data science because it ensures the accuracy, consistency, and reliability of the data.

• Data cleaning only focuses on the accuracy of the data.

• Data cleaning is not important in data science.

• Data cleaning is time-consuming and unnecessary in data science.

What is data analysis and why is it crucial in data science?

• Data analysis is the process of collecting and storing data for future use.

• Data analysis is not important in data science as it can be done manually.

• Data analysis is only used for academic research and has no practical applications in the real world.

• Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It is crucial in data science because it helps in understanding the data, identifying patterns and trends, making predictions, and deriving insights that can drive business decisions.

What are some common techniques used for data visualization?

• tables, histograms, network graphs

• box plots, treemaps, parallel coordinates

• bar charts, line charts, scatter plots, pie charts, and heat maps

• word clouds, bubble charts, radar charts

What is the purpose of scatter plots in data science?

• To identify the correlation between variables

• To compare multiple datasets

• To calculate the mean and median of a dataset

• To visualize the distribution of a dataset and identify outliers and spread of the data.

How can data collection be done effectively?

• Data collection can be done effectively by collecting a small sample size.

• Data collection can be done effectively by collecting data from unreliable sources.

• Data collection can be done effectively by randomly selecting data without any specific criteria.

• Data collection can be done effectively by following the steps mentioned in the explanation.

What are some challenges faced during the data cleaning process?

• Missing data, inconsistent data formats, duplicate data, outliers, and handling errors or inconsistencies in the data.

• Data cleaning is not necessary, data cleaning is time-consuming, data cleaning is expensive

• Lack of data quality standards, data duplication, data inconsistency, data normalization issues

• Irrelevant data, incomplete data, incorrect data formats, handling missing values

What are the steps involved in data analysis?

• Defining the problem and objectives, Collecting and cleaning the data, Exploring and analyzing the data, Drawing conclusions and making predictions, Communicating the results

• Collecting and cleaning the data, Exploring and analyzing the data, Drawing conclusions and making assumptions, Communicating the results

• Defining the problem and objectives, Exploring and analyzing the data, Drawing conclusions and making predictions, Communicating the results

• Collecting and analyzing the data, Drawing conclusions and making assumptions, Communicating the results

What is the purpose of data visualization in data science?

• To make data more confusing and difficult to understand.

• To hide patterns, trends, and relationships within the data.

• To present data in a written format instead of visual format.

• To present data in a visual format and help understand patterns, trends, and relationships within the data.